The points show the inelasticity, while the blue and green curves show the standard model predictions for neutrinos and antineutrinos respectively. The red curve shows the expectation for the mixture expected in IceCube. For aficionados, this calculation is done at next-to-leading order accuracy, with BFKL evolution to low-x partons.
This measurement is sensitive both to potential beyond-standard-model physics, which would likely have a rather different inelasticity distribution than for the expected interactions. Even the standard model cross-section is sensitive to the number of low-momentum quarks and antiquarks in the target nucleus.
Inelasticity is interesting in it's own right. But, the inelasticity can also be used to probe a number of additional physics topics. Neutrinos and antineutrinos have different inelasticity distributions, so by assuming the standard model values, we can measure the neutrino:antineutrino ratio. As can be inferred from the plot above, it is exactly as expected. Unfortunately, at the energies where IceCube is sensitive, we are mostly studying atmospheric neutrinos, not astrophysical.
We can also use inelasticity to probe astrophysical neutrinos. Although the neutrinos selected here are mostly muon neutrinos, some tau neutrinos make it into the fit, and it is possible to use similar criteria to select a matching set of cascades. The plot below shows the flavor triangle found from this study.
Each point on the flavor triangle corresponds to a unique mixture of electron, muon and tau neutrinos. The upper point is all muon neutrino, with the lower left and lower-right points corresponding to all tau neutrinos and all electron neutrinos respectively. The colors show the relatively likelihood, with the best-fit point (cross) corresponding to 83% tau neutrino, 17% electron neutrino and no muon neutrino. Unfortunately, the errors are large, so none of the different standard acceleration scenarios can be ruled out.
This work was done by my student, Gary Binder. Besides the IceCube paper, he wrote a very nice dissertation, available here. For this work, he won the GNN (Global Neutrino Network) dissertation prize for 2018.
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